Benefits and limitations of Kaplan–Meier calculations of survival chance in cancer surgery (original) (raw)

Abstract

Background and aim

Especially in malign diseases, the therapeutic decision depends on the prognosis for the individual patient. A prognosis is a prediction of the future course of disease following its onset. Graphical representation of such statistical results—such as the well-known Kaplan–Meier curve—is often used to assist readers of a paper in the interpretation. However, mistakes and distortions frequently arise in the display and interpretation of survival plots. This review aims to highlight such pitfalls and provide recommendations for future practice.

Methods

Special topics are discussed: the criteria for the presentation of the survival curve, the problem of missing values, estimation of the prognosis in the presence of competing risks, comparison of treatment effects and analysis of survival by tumour-response category.

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References

  1. Kaplan EL, Meier P (1958) Nonparametric estimation from incomplete observations. J Am Stat Assoc 53:457–481
    Google Scholar
  2. Altman DG, De Stavola BL, Love SB, Stepniewska KA (1995) Review of survival analyses published in cancer journals. Br J Cancer 72:511–518
    CAS PubMed Google Scholar
  3. Bonenkamp JJ, Hermans J, Sasako M, van de Velde CJ (1999) Extended lymph-node dissection for gastric cancer. Dutch Gastric Cancer Group. N Engl J Med 340:908–914
    Article CAS PubMed Google Scholar
  4. Hartgrink HH, van de Velde CJH on behalf of the Dutch Gastric Cancer Group (2003) Final results of the Dutch D1 versus D2 gastric cancer trial. In: Santoro E, Garofalo A (eds) Proceedings of the 5th World Gastric Cancer Congress. International Gastric Cancer Congress (ROM)
  5. Bollschweiler E, Schneider PM, Monig SP, Altendorf-Hofmann A, Mansmann U, Lehmacher W, Schlag PM, Merkel S, Hohenberger W, Izbicki JR, Hermanek P, Holscher AH (2003) Prognostic relevance of biological and molecular markers in oncology. Criteria for planning and interpreting studies. Chirurg 74:139–144
    CAS PubMed Google Scholar
  6. Pocock SJ, Clayton TC, Altman DG (2002) Survival plots of time-to-event outcomes in clinical trials: good practice and pitfalls. Lancet 359:1686–1689
    Article PubMed Google Scholar
  7. Brookmeyer R, Crowley J (1982) A confidence interval for the median survival time. Biometrics 38:29–41
    Google Scholar
  8. Shuster JJ (1991) Median follow-up in clinical trials. J Clin Oncol 9:191–192
    CAS PubMed Google Scholar
  9. Kalbfleisch JD, Prentice RL (1980) The statistical analysis of failure time data. Wiley, New York
  10. Clark TG, Altman DG, De Stavola BL (2002) Quantification of the completeness of follow-up. Lancet 359:1309–1310
    Article PubMed Google Scholar
  11. Cantor AB, Shuster JJ (1992) Parametric versus non-parametric methods for estimating cure rates based on censored survival data. Stat Med 11:931–937
    CAS PubMed Google Scholar
  12. Andersen J, Goetghebeur E, Ryan L (1996) Missing cause of death information in the analysis of survival data. Stat Med 15:2191–2201
    Article CAS PubMed Google Scholar
  13. Flehinger BJ, Reiser B, Yashchin E (2002) Parametric modelling for survival with competing risks and masked failure causes. Lifetime Data Anal 8:177–203
    Article PubMed Google Scholar
  14. Brookmeyer R, Curriero FC (2002) Survival curve estimation with partial non-random exposure. Stat Med 21:2671–2683
    Article PubMed Google Scholar
  15. Koscielny S, Thames HD (2001) Biased methods for estimating local and distant failure rates in breast carcinoma and a "commonsense" approach. Cancer 92:2220–2227
    Article CAS PubMed Google Scholar
  16. Gelman R, Gelber R, Henderson IC, Coleman CN, Harris JR (1990) Improved methodology for analyzing local and distant recurrence. J Clin Oncol 8:548–555
    CAS PubMed Google Scholar
  17. Anderson JR (2001) Commonly misused approaches in the analysis of cancer clinical trials. In: Crowley J (ed) Handbook of statistics in clinical oncology
  18. Weiss GB, Bunce H, Hokanson JA (1983) Comparing survival of responders and nonresponders after treatment: a potential source of confusion in interpreting cancer clinical trials. Control Clin Trials 4:43–52
    CAS PubMed Google Scholar
  19. Anderson JR, Cain KC, Gelber RD (1983) Analysis of survival by tumor response. J Clin Oncol 1:710–719
    CAS PubMed Google Scholar
  20. Anderson JR, Cain KC, Gelber RD, Gelman RS (1985) Analysis and interpretation of the comparison of survival by treatment outcome variables in cancer clinical trials. Cancer Treat Rep 69:1139–1146
    CAS PubMed Google Scholar
  21. Simon R, Wittes RE (1985) Methodologic guidelines for reports of clinical trials. Cancer Treat Rep 69:1–3
    CAS PubMed Google Scholar
  22. Simon R, Makuch RW (1984) A non-parametric graphical representation of the relationship between survival and the occurrence of an event: application to responder versus non-responder bias. Stat Med 3:35–44
    CAS PubMed Google Scholar

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Authors and Affiliations

  1. Department of Visceral and Vascular Surgery, University of Cologne, Joseph Stelzmann Strasse 9, 50931, Cologne, Germany
    Elfriede Bollschweiler

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  1. Elfriede Bollschweiler
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Correspondence toElfriede Bollschweiler.

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Bollschweiler, E. Benefits and limitations of Kaplan–Meier calculations of survival chance in cancer surgery.Langenbecks Arch Surg 388, 239–244 (2003). https://doi.org/10.1007/s00423-003-0410-6

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